Update app.py

app.py

@@ -1,63 +1,431 @@
+import os
+import subprocess
+
+# Install flash attention
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+
+import copy
+import spaces
+import time
+import torch
+
+from threading import Thread
+from typing import List, Dict, Union
+import urllib
+from PIL import Image
+import io
+import datasets
+
 import gradio as gr
+from transformers import AutoProcessor, TextIteratorStreamer
+from transformers import Idefics2ForConditionalGeneration
+import tempfile
+from streaming_stt_nemo import Model
 from huggingface_hub import InferenceClient
+import edge_tts
+import asyncio
+
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+
+default_lang = "en"
+
+engines = { default_lang: Model(default_lang) }
+
+def transcribe(audio):
+    lang = "en"
+    model = engines[lang]
+    text = model.stt_file(audio)[0]
+    return text
+
+client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+system_instructions1 = "<s>[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses as if You are the character Jarvis, made by 'Tony Stark.' The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
+
+def model(text):
+    generate_kwargs = dict(
+        temperature=0.7,
+        max_new_tokens=512,
+        top_p=0.95,
+        repetition_penalty=1,
+        do_sample=True,
+        seed=42,
+    )
+    
+    formatted_prompt = system_instructions1 + text + "[JARVIS]"
+    stream = client1.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        if not response.token.text == "</s>":
+            output += response.token.text
+
+    return output
+
+async def respond(audio):
+    user = transcribe(audio)
+    reply = model(user)
+    communicate = edge_tts.Communicate(reply)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    yield tmp_path
+
+DEVICE = torch.device("cuda")
+MODELS = {
+    "idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
+        "HuggingFaceM4/idefics2-8b-chatty",
+        torch_dtype=torch.bfloat16,
+        _attn_implementation="flash_attention_2",
+    ).to(DEVICE),
+}
+PROCESSOR = AutoProcessor.from_pretrained(
+    "HuggingFaceM4/idefics2-8b",
+)
+
+SYSTEM_PROMPT = [
+    {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": "The following is a conversation between Idefics2, a highly knowledgeable and intelligent visual AI assistant created by Hugging Face, referred to as Assistant, and a human user called User. In the following interactions, User and Assistant will converse in natural language, and Assistant will do its best to answer User’s questions. Assistant has the ability to perceive images and reason about them, but it cannot generate images. Assistant was built to be respectful, polite and inclusive. It knows a lot, and always tells the truth. When prompted with an image, it does not make up facts. If required you can also generate images, always generate image in this format ![](https://image.pollinations.ai/prompt/{description}?width={width}&height={height}&nologo=poll&nofeed=yes&seed={random}), where {description} is: {StyleofImage}%20{PromptifiedPrompt}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}, where [random] is: Random 10-digit positive integer",
+            },
+        ],
+    },
+    {
+        "role": "assistant",
+        "content": [
+            {
+                "type": "text",
+                "text": "Hello, I'm Idefics2, Huggingface's latest multimodal assistant. How can I help you?",
+            },
+        ],
+    }
+]
+
+BOT_AVATAR = "OpenAI_logo.png"
+
+
+# Chatbot utils
+def turn_is_pure_media(turn):
+    return turn[1] is None
+
+
+def load_image_from_url(url):
+    with urllib.request.urlopen(url) as response:
+        image_data = response.read()
+        image_stream = io.BytesIO(image_data)
+        image = Image.open(image_stream)
+        return image
+
+
+def img_to_bytes(image_path):
+    image = Image.open(image_path).convert(mode='RGB')
+    buffer = io.BytesIO()
+    image.save(buffer, format="JPEG")
+    img_bytes = buffer.getvalue()
+    image.close()
+    return img_bytes
+
+
+def format_user_prompt_with_im_history_and_system_conditioning(
+    user_prompt, chat_history
+) -> List[Dict[str, Union[List, str]]]:
+    """
+    Produces the resulting list that needs to go inside the processor.
+    It handles the potential image(s), the history and the system conditionning.
+    """
+    resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
+    resulting_images = []
+    for resulting_message in resulting_messages:
+        if resulting_message["role"] == "user":
+            for content in resulting_message["content"]:
+                if content["type"] == "image":
+                    resulting_images.append(load_image_from_url(content["image"]))
+
+    # Format history
+    for turn in chat_history:
+        if not resulting_messages or (
+            resulting_messages and resulting_messages[-1]["role"] != "user"
+        ):
+            resulting_messages.append(
+                {
+                    "role": "user",
+                    "content": [],
+                }
+            )
+
+        if turn_is_pure_media(turn):
+            media = turn[0][0]
+            resulting_messages[-1]["content"].append({"type": "image"})
+            resulting_images.append(Image.open(media))
+        else:
+            user_utterance, assistant_utterance = turn
+            resulting_messages[-1]["content"].append(
+                {"type": "text", "text": user_utterance.strip()}
+            )
+            resulting_messages.append(
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": user_utterance.strip()}],
+                }
+            )
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+    # Format current input
+    if not user_prompt["files"]:
+        resulting_messages.append(
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": user_prompt["text"]}],
+            }
+        )
+    else:
+        # Choosing to put the image first (i.e. before the text), but this is an arbiratrary choice.
+        resulting_messages.append(
+            {
+                "role": "user",
+                "content": [{"type": "image"}] * len(user_prompt["files"])
+                + [{"type": "text", "text": user_prompt["text"]}],
+            }
+        )
+        resulting_images.extend([Image.open(path) for path in user_prompt["files"]])
 
+    return resulting_messages, resulting_images
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
+
+def extract_images_from_msg_list(msg_list):
+    all_images = []
+    for msg in msg_list:
+        for c_ in msg["content"]:
+            if isinstance(c_, Image.Image):
+                all_images.append(c_)
+    return all_images
+
+
+@spaces.GPU(duration=60)
+def model_inference(
+    user_prompt,
+    chat_history,
+    model_selector,
+    decoding_strategy,
     temperature,
+    max_new_tokens,
+    repetition_penalty,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    if user_prompt["text"].strip() == "" and not user_prompt["files"]:
+        gr.Error("Please input a query and optionally image(s).")
+
+    if user_prompt["text"].strip() == "" and user_prompt["files"]:
+        gr.Error("Please input a text query along the image(s).")
+
+    streamer = TextIteratorStreamer(
+        PROCESSOR.tokenizer,
+        skip_prompt=True,
+        timeout=120.0,
+    )
+
+    generation_args = {
+        "max_new_tokens": max_new_tokens,
+        "repetition_penalty": repetition_penalty,
+        "streamer": streamer,
+    }
+
+    assert decoding_strategy in [
+        "Greedy",
+        "Top P Sampling",
+    ]
+    if decoding_strategy == "Greedy":
+        generation_args["do_sample"] = False
+    elif decoding_strategy == "Top P Sampling":
+        generation_args["temperature"] = temperature
+        generation_args["do_sample"] = True
+        generation_args["top_p"] = top_p
+
+    # Creating model inputs
+    (
+        resulting_text,
+        resulting_images,
+    ) = format_user_prompt_with_im_history_and_system_conditioning(
+        user_prompt=user_prompt,
+        chat_history=chat_history,
+    )
+    prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
+    inputs = PROCESSOR(
+        text=prompt,
+        images=resulting_images if resulting_images else None,
+        return_tensors="pt",
+    )
+    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+    generation_args.update(inputs)
+
+    thread = Thread(
+        target=MODELS[model_selector].generate,
+        kwargs=generation_args,
+    )
+    thread.start()
+
+    print("Start generating")
+    acc_text = ""
+    for text_token in streamer:
+        time.sleep(0.01)
+        acc_text += text_token
+        if acc_text.endswith("<end_of_utterance>"):
+            acc_text = acc_text[:-18]
+        yield acc_text
+    print("Success - generated the following text:", acc_text)
+    print("-----")
+
+
+FEATURES = datasets.Features(
+    {
+        "model_selector": datasets.Value("string"),
+        "images": datasets.Sequence(datasets.Image(decode=True)),
+        "conversation": datasets.Sequence({"User": datasets.Value("string"), "Assistant": datasets.Value("string")}),
+        "decoding_strategy": datasets.Value("string"),
+        "temperature": datasets.Value("float32"),
+        "max_new_tokens": datasets.Value("int32"),
+        "repetition_penalty": datasets.Value("float32"),
+        "top_p": datasets.Value("int32"),
+        }
+    )
+
+
+# Hyper-parameters for generation
+max_new_tokens = gr.Slider(
+    minimum=512,
+    maximum=4096,
+    value=1024,
+    step=1,
+    interactive=True,
+    label="Maximum number of new tokens to generate",
+)
+repetition_penalty = gr.Slider(
+    minimum=0.01,
+    maximum=5.0,
+    value=1.1,
+    step=0.01,
+    interactive=True,
+    label="Repetition penalty",
+    info="1.0 is equivalent to no penalty",
+)
+decoding_strategy = gr.Radio(
+    [
+        "Greedy",
+        "Top P Sampling",
     ],
+    value="Greedy",
+    label="Decoding strategy",
+    interactive=True,
+    info="Higher values is equivalent to sampling more low-probability tokens.",
+)
+temperature = gr.Slider(
+    minimum=0.0,
+    maximum=5.0,
+    value=0.4,
+    step=0.1,
+    visible=False,
+    interactive=True,
+    label="Sampling temperature",
+    info="Higher values will produce more diverse outputs.",
+)
+top_p = gr.Slider(
+    minimum=0.01,
+    maximum=0.99,
+    value=0.8,
+    step=0.01,
+    visible=False,
+    interactive=True,
+    label="Top P",
+    info="Higher values is equivalent to sampling more low-probability tokens.",
+)
+
+
+chatbot = gr.Chatbot(
+    label="Idefics2-Chatty",
+    avatar_images=[None, BOT_AVATAR],
+    height=450,
+    show_copy_button=True, 
+    likeable=True, 
+    layout="panel"
 )
 
+output=gr.Textbox(label="Prompt")
+
+with gr.Blocks(
+    fill_height=True,
+    css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
+) as img:
+
+    gr.Markdown("# Image Chat, Image Generation, Image classification and Normal Chat")
+    with gr.Row(elem_id="model_selector_row"):
+        model_selector = gr.Dropdown(
+            choices=MODELS.keys(),
+            value=list(MODELS.keys())[0],
+            interactive=True,
+            show_label=False,
+            container=False,
+            label="Model",
+            visible=False,
+        )
+
+    decoding_strategy.change(
+        fn=lambda selection: gr.Slider(
+            visible=(
+                selection
+                in [
+                    "contrastive_sampling",
+                    "beam_sampling",
+                    "Top P Sampling",
+                    "sampling_top_k",
+                ]
+            )
+        ),
+        inputs=decoding_strategy,
+        outputs=temperature,
+    )
+    decoding_strategy.change(
+        fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
+        inputs=decoding_strategy,
+        outputs=top_p,
+    )
+
+    gr.ChatInterface(
+        fn=model_inference,
+        chatbot=chatbot,
+        multimodal=True,
+        cache_examples=False,
+        additional_inputs=[
+            model_selector,
+            decoding_strategy,
+            temperature,
+            max_new_tokens,
+            repetition_penalty,
+            top_p,
+        ],
+    )
+
+with gr.Blocks() as voice:   
+    with gr.Row():
+        input = gr.Audio(label="Voice Chat", sources="microphone", type="filepath", waveform_options=False)
+        output = gr.Audio(label="AI", type="filepath",
+                        interactive=False,
+                        autoplay=True,
+                        elem_classes="audio")
+        gr.Interface(
+            fn=respond, 
+            inputs=[input],
+                outputs=[output], live=True)
+ 
+with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none}", title="GPT 4o DEMO") as demo:
+    gr.TabbedInterface([img, voice], ['💬 SuperChat','🗣️ Voice Chat', ])
+
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()